Integrated Likelihood Inference in Small Sample Meta‐analysis for Continuous Outcomes

This paper proposes the use of the integrated likelihood for inference on the mean effect in small‐sample meta‐analysis for continuous outcomes. The method eliminates the nuisance parameters given by variance components through integration with respect to a suitable weight function, with no need to estimate them. The integrated likelihood approach takes into proper account the estimation uncertainty of within‐study variances, thus providing confidence intervals with empirical coverage closer to nominal levels than standard likelihood methods. The improvement is remarkable when either (i) the number of studies is small to moderate or (ii) the small sample size of the studies does not allow to consider the within‐study variances as known, as common in applications. Moreover, the use of the integrated likelihood avoids numerical pitfalls related to the estimation of variance components which can affect alternative likelihood approaches. The proposed methodology is illustrated via simulation and applied to a meta‐analysis study in nutritional science.     

A test of risk vulnerability in the wider population

Theory and Decision - Panel data from the German SOEP is used to test for risk vulnerability (RV) in the wider population. Two different survey responses are analysed: the response to the question...     

London congestion charge: the impact on air pollution and school attendance by socioeconomic status

Population and Environment - Air pollution negatively affects individuals’ health and human capital accumulation. For example, students’ school performance is hampered by air pollution...     

Women Left Behind? Poverty and Headship in Africa

Two stylized facts about poverty in Africa motivate this article: female-headed households tend to be poorer, and poverty has been falling in the aggregate since the 1990s. These facts raise two questions. First, how have female-headed households fared? Second, what role have they played in Africa’s impressive recent aggregate growth and poverty reduction? Using data covering the entire region, we reexamine the current prevalence and characteristics of female-headed households and ask whether their prevalence has been rising, what factors have been associated with such changes since the mid-1990s, and whether poverty has fallen equiproportionately for male- and female-headed households. Lower female headship is associated with higher gross domestic product. However, other subtle transformations occurring across Africa—changes in marriage behavior, family formation, health, and education—are positively related to female headship, resulting in a growing share of female-headed households. This shift has been happening alongside declining aggregate poverty incidence. However, rather than being left behind, female-headed households have generally seen faster poverty reduction. As a whole, this group has contributed substantially to the reduction in poverty despite their smaller share in the population.     

The econometric modelling of social preferences

Experimental data on social preferences present a number of features that need to be incorporated in econometric modelling. We explore a variety of econometric modelling approaches to the analysis of such data. The approaches under consideration are: the Random Utility approach (in which it is assumed that each possible action yields a utility with a deterministic and a stochastic component, and that the individual selects the action yielding the highest utility); the Random Behavioural approach (which assumes that the individual computes the maximum of a deterministic utility function, and that computational error causes their observed behaviour to depart stochastically from this optimum); and the Random Preference approach (in which all variation in behaviour is attributed to stochastic variation in the parameters of the deterministic component of utility). These approaches are applied in various ways to an experiment on fairness conducted by Cappelen et al. (Am Econ Rev 97(3):818–827, 2007). Various models that we estimate succeed in capturing the key features of the dataset. Conclusions concerning fairness-related behaviour depend crucially on the choice of econometric model.     

Modelling the role of variables in model-based cluster analysis

In the framework of cluster analysis based on Gaussian mixture models, it is usually assumed that all the variables provide information about the clustering of the sample units. Several variable selection procedures are available in order to detect the structure of interest for the clustering when this structure is contained in a variable sub-vector. Currently, in these procedures a variable is assumed to play one of (up to) three roles: (1) informative, (2) uninformative and correlated with some informative variables, (3) uninformative and uncorrelated with any informative variable. A more general approach for modelling the role of a variable is proposed by taking into account the possibility that the variable vector provides information about more than one structure of interest for the clustering. This approach is developed by assuming that such information is given by non-overlapped and possibly correlated sub-vectors of variables; it is also assumed that the model for the variable vector is equal to a product of conditionally independent Gaussian mixture models (one for each variable sub-vector). Details about model identifiability, parameter estimation and model selection are provided. The usefulness and effectiveness of the described methodology are illustrated using simulated and real datasets.     

Demographic and territorial characteristics of COVID-19 cases and excess mortality in the European Union during the first wave

Journal of Population Research - This article explores for a large number of countries in the European Union (plus the United Kingdom) the main demographic differentials in positive tested COVID-19...